Client Report - Project 0: Introduction

Table of contents

• QUESTION|TASK 1
• QUESTION|TASK 2

import pandas as pd
import numpy as np
from lets_plot import *
from palmerpenguins import load_penguins


LetsPlot.setup_html(isolated_frame=True)

# Learn more about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html

# Include and execute your code here
from palmerpenguins import load_penguins
df = load_penguins()

QUESTION|TASK 1

Include the tables created from PY4DS: CH2 Data Visualization used to create the above chart (Hint: copy the code from 2.2.1. The penguins data frame and paste each in the cells below)

# Include and execute your code here
penguins = load_penguins()
penguins

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007
...	...	...	...	...	...	...	...	...
339	Chinstrap	Dream	55.8	19.8	207.0	4000.0	male	2009
340	Chinstrap	Dream	43.5	18.1	202.0	3400.0	female	2009
341	Chinstrap	Dream	49.6	18.2	193.0	3775.0	male	2009
342	Chinstrap	Dream	50.8	19.0	210.0	4100.0	male	2009
343	Chinstrap	Dream	50.2	18.7	198.0	3775.0	female	2009

344 rows × 8 columns

These tables contain all the data on species of penguins that will be displayed in the graphs.

# Include and execute your code here
penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007

.head() shows the first five rows of data from the first table.

QUESTION|TASK 2

Recreate the example charts from PY4DS: CH2 Data Visualization of the textbook. (Hint: copy the chart code from 2.2.3. Creating a Plot, one for each cell below)

# Include and execute your code here
(
    ggplot(data=penguins, 
    mapping=aes(x="flipper_length_mm", y="body_mass_g"))
    + geom_point()
)

I can see the positive correlation between flipper length and body mass after running this code (a longer flipper length seems to indicate a larger body mass).

# Include and execute your code here
(
    ggplot(
        data=penguins,
        mapping=aes(x="flipper_length_mm", y="body_mass_g", color="species"),
    )
    + geom_point()
)

This color=“species” color codes the points for the three different species of penguins

# Include and execute your code here
(
    ggplot(
        data=penguins,
        mapping=aes(x="flipper_length_mm", y="body_mass_g", color="species"),
    )
    + geom_point()
    + geom_smooth(method="lm")
)

geom_smooth added lines for each species to better see the positive correlation

# Include and execute your code here
(
    ggplot(data=penguins, mapping=aes(x="flipper_length_mm", y="body_mass_g"))
    + geom_point(aes(color="species", shape="species"))
    + geom_smooth(method="lm")
    + labs(
        title="Body mass and flipper length",
        subtitle="Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
        x="Flipper length (mm)",
        y="Body mass (g)",
        color="Species",
        shape="Species",
    )
)

The labs code adds all the labels for the title, subtitle, x- and y- axis, and species key

# Include and execute your code here
(
    ggplot(penguins, aes(x="flipper_length_mm", y="body_mass_g"))
    + geom_point(aes(color="species", shape="species"))
    + facet_wrap(facets="island")
)

The facet_wrap divided the data into three charts determined by island so the chart is more readable.